| 滑动窗口模型下的优化数据流聚类算法 |
| |
| 引用本文： | 胡彧,闫巧梅. 滑动窗口模型下的优化数据流聚类算法[J]. 计算机应用, 2008, 28(6): 1414-1416 |
| |
| 作者姓名： | 胡彧 闫巧梅 |
| |
| 作者单位： | 太原理工大学,测控技术研究所,太原,030024;太原理工大学,计算机与软件学院,太原,030024 |
| |
| 基金项目： | 国家自然科学基金 , 山西省教育厅高校科技研发项目 |
| |
| 摘 要： | 为提高对进化数据流的聚类质量及效率，采用聚类特征指数直方图支持数据处理，减少直方图结构的维护数,改进滑动窗口下的流数据聚类算法。实验表明，与传统基于界标模型的聚类算法相比，优化算法可获得较好的工作效率、较小的内存开销和快速的数据处理能力，拓展了流数据挖掘技术的应用领域。
|
| 关 键 词： | 数据流 滑动窗口 聚类 直方图 数据挖掘 |
| 文章编号： | 1001-9081(2008)06-1414-03 |
| 收稿时间： | 2007-12-17 |
| 修稿时间： | 2007-12-17 |
Optimal algorithm of data streams clustering on sliding window model |
| |
| HU Yu,YAN Qiao-mei. Optimal algorithm of data streams clustering on sliding window model[J]. Journal of Computer Applications, 2008, 28(6): 1414-1416 |
| |
| Authors: | HU Yu YAN Qiao-mei |
| |
| Affiliation: | HU Yu1,YAN Qiao-mei21.Institute of Measuring , Controlling Technology,Taiyuan University of Technology,Taiyuan Shanxi 030024,China,2.College of Computer , Software |
| |
| Abstract: | Streaming data clustering algorithm was improved based on sliding window model with the purpose of high cluster quality and efficiency. The new algorithm adopted cluster feature histograms as the data supporting structure. Compared with traditional cluster algorithms, the complexity and clustering effect of new algorithm was further improved. In addition, the algorithm is of better efficiency, less memory overhead and fast data processing capabilities, and the application of data streaming mining technologies is expanded. |
| |
| Keywords: | data streams sliding window cluster histogram data mining |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机应用》浏览原始摘要信息 |
|
点击此处可从《计算机应用》下载全文 |
|
